Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2005 May 15;105(3-4):277-87.
doi: 10.1016/j.vetimm.2005.02.015.

Molecular analyses of disease pathogenesis: application of bovine microarrays

Affiliations
Review

Molecular analyses of disease pathogenesis: application of bovine microarrays

Heather L Wilson et al. Vet Immunol Immunopathol. .

Abstract

The molecular analysis of disease pathogenesis in cattle has been limited by the lack of availability of tools to analyze both host and pathogen responses. These limitations are disappearing with the advent of methodologies such as microarrays that facilitate rapid characterization of global gene expression at the level of individual cells and tissues. The present review focuses on the use of microarray technologies to investigate the functional pathogenomics of infectious disease in cattle. We discuss a number of unique issues that must be addressed when designing both in vitro and in vivo model systems to analyze host responses to a specific pathogen. Furthermore, comparative functional genomic strategies are discussed that can be used to address questions regarding host responses that are either common to a variety of pathogens or unique to individual pathogens. These strategies can also be applied to investigations of cell signaling pathways and the analyses of innate immune responses. Microarray analyses of both host and pathogen responses hold substantial promise for the generation of databases that can be used in the future to address a wide variety of questions. A critical component limiting these comparative analyses will be the quality of the databases and the complete functional annotation of the bovine genome. These limitations are discussed with an indication of future developments that will accelerate the validation of data generated when completing a molecular characterization of disease pathogenesis in cattle.

PubMed Disclaimer

Figures

Fig. 1
Fig. 1
A Venn diagram showing the number of differentially expressed genes for studies on (A) effect of commensal microflora on host responses to enteric viral infection observed 24 h after corona infection in antibiotic flushed (−microflora) or in “loops” containing ingesta (+microflora), (B) comparative gene expression analyses between in vivo bovine rotavirus and coronavirus infections in young calves, (C) comparative gene expression analyses between in vivo and in vitro bovine rotavirus infections. The numbers in circles indicate the altered genes (p < 0.2 and 1.2-fold change) for each condition and those at the intersection indicate the genes common between conditions.
Fig. 2
Fig. 2
Comparison of fluorescence and RLS microarray technology. (A) Partial Tiff images of the same microarray grids after scanning hybridized bovine microarrays are shown in pseudo colors. Fluorescence scanned arrays were hybridized with Cy3/Cy5 dye labeled ‘control’ and ‘sample’ cDNA, respectively (left panel) and RLS scanned arrays were hybridized with silver/gold labeled ‘control’ and ‘sample’ cDNA, respectively (right panel). The amount of total RNA used for fluorescence and RLS techniques was 5 and 1 μg, respectively. (B) Scatter plot of processed and normalized intensities for gene expression as observed in fluorescence (left panel) and RLS analysis (right panel). Lines above and below the center line denote the limits for 2-fold up or down regulation of gene expression and points between these lines are considered unchanged between ‘Control’ and ‘Sample’.

Similar articles

Cited by

References

    1. Aich, P., Wilson, H.L., Rawlyk, N., Jalal, S., Kaushik, R.S., Begg, A.A., Potter, A., Babiuk, L.A., Abrahamsen, M.S., Griebel, P., 2005. Microarray analysis of gene expression following preparation of sterile intestinal “loops” in calves. Can. J. Anim. Sci., in press.
    1. Ball C.A., Sherlock G., Parkinson H., Rocca-Sera P., Brooksbank C., Causton H.C., Cavalieri D., Gaasterland T., Hingamp P., Holstege F., Ringwald M., Spellman P., Stoeckert C.J., Jr., Stewart J.E., Taylor R., Brazma A., Quackenbush J. Standards for microarray data. Science. 2002;298(5593):539. - PubMed
    1. Band M.R., Olmstead C., Everts R.E., Liu Z.L., Lewin H.A. A 3800 gene microarray for cattle functional genomics: comparison of gene expression in spleen, placenta, and brain. Anim. Biotechnol. 2002;13(1):163–172. - PubMed
    1. Bao P., Frutos A.G., Greef C., Lahiri J., Muller U., Peterson T.C., Warden L., Xie X. High-sensitivity detection of DNA hybridization on microarrays using resonance light scattering. Anal. Chem. 2002;74(8):1792–1797. - PubMed
    1. Barlow C., Lockhart D.J. DNA arrays and neurobiology—what's new and what's next? Curr. Opin. Neurobiol. 2002;12(5):554–561. - PubMed

Publication types

MeSH terms

LinkOut - more resources